OLMo-1B-hf
| Property | Value |
|---|---|
| Parameter Count | 1.18B |
| Training Tokens | 3 Trillion |
| Context Length | 2048 |
| License | Apache 2.0 |
| Paper | arXiv:2402.00838 |
What is OLMo-1B-hf?
OLMo-1B-hf is part of the Open Language Model (OLMo) series developed by Allen AI to advance language model science. This Hugging Face-compatible version features 16 layers, 2048 hidden size, and 16 attention heads, trained on the comprehensive Dolma dataset.
Implementation Details
The model utilizes a modern Transformer architecture with several optimizations:
- Non-parametric LayerNorm and RoPE positional embeddings
- Full attention mechanism with sequential block type
- SwiGLU activation function
- Training optimized with AdamW (lr=4.0E-4, weight decay=0.1)
Core Capabilities
- Strong performance on core NLP tasks (62.42% average across standard benchmarks)
- Competitive with larger models on some tasks
- Efficient text generation with support for various sampling methods
- Easy integration with Hugging Face Transformers library
Frequently Asked Questions
Q: What makes this model unique?
OLMo-1B-hf stands out for its complete transparency in training data, methodology, and evaluation metrics. It achieves impressive performance for its size class, particularly in tasks like COPA (79%) and PIQA (73.7%).
Q: What are the recommended use cases?
The model is well-suited for research purposes, text generation tasks, and as a foundation for fine-tuning on specific applications. It's particularly effective for tasks requiring strong reasoning capabilities within its 2048 token context window.
